Cluster Evolution Analytics

Morales-Oñate, Víctor and Morales-Oñate, Bolívar (2024): Cluster Evolution Analytics.

Abstract

In this paper we propose Cluster Evolution Analytics (CEA) as a framework that can be considered in the realm of Advanced Exploratory Data Analysis or unsupervised learning. CEA leverages on the temporal component of panel data and it is based on combining two techniques that are usually not related: leave-one-out and plug-in principle. This allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. We illustrate our results on a real dataset applying CEA on different clustering algorithms and developed a Shiny App with a particular configuration. Finally, we also provide an R package so that this framework can be used on different applications.

• Item Type: MPRA Paper
• Language: English
• Keywords: clustering, temporal clustering, statistical profiles
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling

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